# Multi-Differential Fairness Auditor for Black Box Classifiers

arXiv: https://arxiv.org/abs/1903.07609 · HTML: https://mlsanity.com/paper/1903.07609/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [Fair Representations by Compression](https://mlsanity.com/paper/2105.14044/) (2021)
- [Bias and unfairness in machine learning models: a systematic literature review](https://mlsanity.com/paper/2202.08176/) (2022)
- [Ex-Ante Assessment of Discrimination in Dataset](https://mlsanity.com/paper/2208.07918/) (2022)

## Abstract (arXiv)

Machine learning algorithms are increasingly involved in sensitive decision-making process with adversarial implications on individuals. This paper presents mdfa, an approach that identifies the characteristics of the victims of a classifier's discrimination. We measure discrimination as a violation of multi-differential fairness. Multi-differential fairness is a guarantee that a black box classifier's outcomes do not leak information on the sensitive attributes of a small group of individuals. We reduce the problem of identifying worst-case violations to matching distributions and predicting where sensitive attributes and classifier's outcomes coincide. We apply mdfa to a recidivism risk assessment classifier and demonstrate that individuals identified as African-American with little criminal history are three-times more likely to be considered at high risk of violent recidivism than similar individuals but not African-American.